


from io import BytesIO
from typing import List, Union

import requests

from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_decord_available():
						import numpy as np
						from decord import VideoReader


if is_torch_available():
						from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

__UpperCamelCase     		=							logging.get_logger(__name__)







@add_end_docstrings(UpperCAmelCase   )
class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			def __init__(				self   ,    *snake_case   ,    **snake_case  ):

						'''simple docstring'''


						super().__init__(*snake_case   ,    **snake_case  )
						requires_backends(self   ,    "decord"  )
						self.check_model_type(snake_case  )




			def 					snake_case__					(				self   ,    snake_case=None   ,    snake_case=None   ,    snake_case=None  ):

						'''simple docstring'''


						UpperCamelCase__       =							{}
						if frame_sampling_rate is not None:
									UpperCamelCase__       =							frame_sampling_rate
						if num_frames is not None:
									UpperCamelCase__       =							num_frames

						UpperCamelCase__       =							{}
						if top_k is not None:
									UpperCamelCase__       =							top_k
						return preprocess_params, {}, postprocess_params




			def __call__(				self   ,    snake_case   ,    **snake_case  ):

						'''simple docstring'''


						return super().__call__(snake_case   ,    **snake_case  )




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case=None   ,    snake_case=1  ):

						'''simple docstring'''


						if num_frames is None:
									UpperCamelCase__       =							self.model.config.num_frames

						if video.startswith("http://"  ) or video.startswith("https://"  ):
									UpperCamelCase__       =							BytesIO(requests.get(snake_case  ).content  )

						UpperCamelCase__       =							VideoReader(snake_case  )
						videoreader.seek(0  )

						UpperCamelCase__       =							0
						UpperCamelCase__       =							num_frames * frame_sampling_rate - 1
						UpperCamelCase__       =							np.linspace(snake_case   ,    snake_case   ,    num=snake_case   ,    dtype=np.intaa  )

						UpperCamelCase__       =							videoreader.get_batch(snake_case  ).asnumpy()
						UpperCamelCase__       =							list(snake_case  )

						UpperCamelCase__       =							self.image_processor(snake_case   ,    return_tensors=self.framework  )
						return model_inputs




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.model(**snake_case  )
						return model_outputs







			def 					snake_case__					(				self   ,    snake_case   ,    snake_case=5  ):

						'''simple docstring'''


						if top_k > self.model.config.num_labels:
									UpperCamelCase__       =							self.model.config.num_labels

						if self.framework == "pt":
									UpperCamelCase__       =							model_outputs.logits.softmax(-1  )[0]
									UpperCamelCase__,   UpperCamelCase__       =							probs.topk(snake_case  )
						else:
									raise ValueError(F'''Unsupported framework: {self.framework}'''  )

						UpperCamelCase__       =							scores.tolist()
						UpperCamelCase__       =							ids.tolist()
						return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(snake_case   ,    snake_case  )]






def 							UpperCamelCase_(					_A :list[list]	)->		list[list]:
			UpperCamelCase__       =							current_set.copy()
			for row_index, row in enumerate(_A	):
						UpperCamelCase__       =							row[0]
						for column_index, column in enumerate(_A	):
									if magnitude == 0:
												UpperCamelCase__       =							column
												continue
									UpperCamelCase__       =							column / magnitude
    # Subtract to cancel term
			UpperCamelCase__       =							current_set[0]
			UpperCamelCase__       =							[first_row]
			UpperCamelCase__       =							current_set[1::]
			for row in current_set:
						UpperCamelCase__       =							[]
						# If first term is 0, it is already in form we want, so we preserve it
						if row[0] == 0:
									final_set.append(_A	)
									continue
						for column_index in range(len(_A	)	):
									temp_row.append(first_row[column_index] - row[column_index]	)
						final_set.append(_A	)
			# Create next recursion iteration set
			if len(final_set[0]	) != 3:
						UpperCamelCase__       =							final_set[0]
						UpperCamelCase__       =							[]
						UpperCamelCase__       =							[]
						for row in final_set[1::]:
									current_first_column.append(row[0]	)
									next_iteration.append(row[1::]	)
						UpperCamelCase__       =							simplify(_A	)
						for i in range(len(_A	)	):
									resultant[i].insert(0					,  current_first_column[i]	)
						resultant.insert(0					,  _A	)
						UpperCamelCase__       =							resultant
			return final_set
def 							UpperCamelCase_(					_A :list[list]	)->		list:
			if len(_A	) == 0:
						raise IndexError("solve_simultaneous() requires n lists of length n+1"	)
			UpperCamelCase__       =							len(_A	) + 1
			if any(len(_A	) != _length for item in equations	):
						raise IndexError("solve_simultaneous() requires n lists of length n+1"	)
			for row in equations:
						if any(not isinstance(_A					,  (int, float)	) for column in row	):
									raise ValueError("solve_simultaneous() requires lists of integers"	)
			if len(_A	) == 1:
						return [equations[0][-1] / equations[0][0]]
			UpperCamelCase__       =							equations.copy()
			if any(0 in row for row in data_set	):
						UpperCamelCase__       =							data_set.copy()
						UpperCamelCase__       =							[]
						for row_index, row in enumerate(_A	):
									if 0 not in row:
												UpperCamelCase__       =							data_set.pop(_A	)
												break
						if not full_row:
									raise ValueError("solve_simultaneous() requires at least 1 full equation"	)
						data_set.insert(0					,  _A	)
			UpperCamelCase__       =							data_set.copy()
			UpperCamelCase__       =							simplify(_A	)
			UpperCamelCase__       =							simplified[::-1]
			UpperCamelCase__       =							[]
			for row in simplified:
						UpperCamelCase__       =							row[-1]
						if not solutions:
									if row[-2] == 0:
												solutions.append(0	)
												continue
									solutions.append(current_solution / row[-2]	)
									continue
						UpperCamelCase__       =							row.copy()[: len(_A	) - 1 :]
						while temp_row[0] == 0:
									temp_row.pop(0	)
						if len(_A	) == 0:
									solutions.append(0	)
									continue
						UpperCamelCase__       =							temp_row[1::]
						UpperCamelCase__       =							temp_row[::-1]
						for column_index, column in enumerate(_A	):
									current_solution -= column * solutions[column_index]
						solutions.append(_A	)
			UpperCamelCase__       =							[]
			for item in solutions:
						final.append(float(round(_A					,  5	)	)	)
			return final[::-1]


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						__UpperCamelCase     		=							[
						    [2, 1, 1, 1, 1, 4],
						    [1, 2, 1, 1, 1, 5],
						    [1, 1, 2, 1, 1, 6],
						    [1, 1, 1, 2, 1, 7],
						    [1, 1, 1, 1, 2, 8],
						]
						print(solve_simultaneous(eq))
						print(solve_simultaneous([[4, 2]]))


